Evaluation of antiobesogenic properties of fermented foods: In silico insights

Abstract

Obesity prevalence has steadily increased over the past decades. Standard approaches, such as increased energy expenditure, lifestyle changes, a balanced diet, and the use of specific drugs, are the conventional strategies for preventing or treating the disease and its associated complications. Fermented foods and their subsequent bioactive constituents are now believed to be a novel strategy that can complement already existing approaches for managing and preventing this disease. Recent developments in systems biology and bioinformatics have made it possible to model and simulate compounds and disease interactions. The adoption of such in silico models has contributed to the discovery of novel fermented product targets and helped in testing hypotheses regarding the mechanistic impact and underlying functions of fermented food components. From the studies explored, key findings suggest that fermented foods affect adipogenesis, lipid metabolism, appetite regulation, gut microbiota composition, insulin resistance, and inflammation related to obesity, which could lead to new ways to treat these conditions. These outcomes were linked to probiotics, prebiotics, metabolites, and complex bioactive substances produced during fermentation. Overall, fermented foods and their bioactive compounds show promise as innovative tools for obesity management by influencing metabolic pathways and overall gut health.

1. INTRODUCTION

Obesity, a key global public health concern today, is a result of excessive accumulation of excess adipose tissue (Khan et al., 2021) that affects nearly every organ system. Obesity is thought to be a major contributing factor to several chronic‐related non‐communicable diseases (NCDs) (Canfora et al., 2019; Marzullo et al., 2020). As per the WHO, obesity and overweight account for at least 2.8 million deaths annually (WHO, 2021, 2023). Recent studies have reported that more than 1 billion people in the world are living with this NCD (Phelps et al., 2024; Ruze et al., 2023). To date, all nations are affected by obesity, and within the next 10 years, this is expected to become even more pronounced, leading to a greater number of years lost from a healthy life, disability, and death. Therefore, it is critical to take swift action to prevent and treat the prevalence of obesity and its associated metabolic comorbidities (Ruze et al., 2023).

The spectrum of newly developed medical gadgets, pharmacology, lifestyle therapies, and increasingly popular and advanced bariatric operations are among the therapeutic alternatives available today for managing and treating obesity. Although endoscopic and non‐endoscopic surgical procedures have become popular among obese patients, they are still far from perfect due to the inherent risks and complications (Lingvay et al., 2022; Ruze et al., 2023). People frequently use antiobesity medications to avert and cure obesity and associated complications; however, a variety of side effects have prompted the need for safe and efficient natural product alternatives (Jalili et al., 2023). In this case, there is a need to enhance the worldwide population's diet with food products that can combat obesity and other NCDs while boosting a person's health condition. Functional foods or healthy foods (e.g., fermented foods) specially developed and formulated to address chronic illnesses and offer health benefits can partially bridge the gap among drugs, pharmaceuticals, and food, potentially offering therapeutic or disease‐prevention benefits (Misra et al., 2021; Oladimeji & Adebo, 2024; Xiao et al., 2023).

Fermented foods are those that undergo transformation through the process of fermentation, in which microorganisms such as bacteria, molds, or yeasts thrive and catalyze enzymatic changes in the food's components. This microbial activity and subsequent metabolism alter the texture, flavor, and nutritional profile of the food, resulting in unique and often enhanced characteristics (Adebo, 2020; Dimidi et al., 2019). The majority of fermented foods have been shown to prevent obesity, including cabbage‐apple juice (Park et al., 2020), douche (An et al., 2022), probiotic‐fermented blueberry juice (Zhong et al., 2020), huyou juice (Yan et al., 2020), wine pomace (Gerardi et al., 2020), ougan juice (Guo et al., 2021), and Gochujang (Son et al., 2020). Moreover, fermented foods, which are rich in probiotics, prebiotics, and postbiotics, have shown promising results in modulating gut microbiota, improving metabolic health, and reducing obesity‐related inflammation (Jalili et al., 2023). However, obesity is a complicated, multifaceted disease and thus necessitates comprehensive evaluation methods (Lin & Li, 2021).

In silico approaches, involving computational simulations and bioinformatics tools, offer a robust platform for the preliminary assessment of the antiobesogenic properties of fermented foods. These methods can predict the interactions between bioactive compounds and molecular targets involved in obesity pathways, thus providing insights into the potential mechanisms of action. By integrating data from various biological databases, in silico evaluations can identify candidate compounds and prioritize them for further experimental validation (de Medeiros et al., 2024). In nutritional studies such as this, in silico approaches can analyze large datasets and significantly reduce the financial burden associated with traditional experimental research by simulating experiments and predicting outcomes without the need for physical resources (Duarte et al., 2023). In silico approaches minimize the need for animal testing and enable human model simulation by addressing ethical concerns. Additionally, by enabling personalized nutrition strategies and predictive analytics, these models contribute to the development of tailored dietary interventions, ultimately improving public health outcomes.

This review aims to provide an overview of the in silico techniques employed in the evaluation of the antiobesogenic properties of fermented foods. It will discuss the key bioactive components identified in fermented foods, their predicted molecular targets, and the potential mechanisms by which they may exert antiobesogenic effects. Furthermore, it highlights the advantages and limitations of in silico methods and suggests future directions for integrating these approaches with experimental studies to develop effective antiobesogenic interventions.

2. FERMENTED FOODS, BIOACTIVE COMPOUNDS, AND OBESITY MANAGEMENT

Today, thousands of fermented foods and beverages are consumed due to various food matrix and microbe combinations. They are classified using different approaches, with one of the most prevalent methods being based on the type of raw materials used (plant or animal sources), fermentation type (e.g., back‐slopped, spontaneous, or starter culture), and form of fermentation (solid state or submerged). Consequently, fermented foods have been grouped into categories such as vegetables/fruits, cereals, legumes, roots/tubers, dairy, fish/meat, alcoholic beverages, and others. Of these groups, there is limited research available on the impacts of fermented meat and fish on obesity, as these products from their fermentation often contain high levels of salt, preservatives, and other additives. Thus, a detailed summary of various fermented foods, processing methods, models, and associated antiobesogenic effects is presented in Table 1.

TABLE 1.

Selected fermented food groups and their specific use in obesity management.

Fermented food products	Processing method	Models	Specific antiobesogenic effect	References
Fermented dairy products
Milk	Fermentation using LAB	In vitro using 3T3‐L1 preadipocyte, in silico digestion	Inhibit pancreatic lipase	Manzanarez‐Quin et al. (2023)
Milk	Fermentation by Lactobacillus plantarum Q180	Murine 3T3‐L1 preadipocyte	Inhibit pancreatic lipase	Kim and Lim (2020)
Kefir	Fermentation by Lactobacillus spp., Yeast spp.	Asymptomatic overweight adults	Improvement in serum zonulin level and general mood of the participants	Pražnikar et al. (2020)
		Wistar female rats	Reduced low‐density lipoprotein by 24%–66%, raised blood glucose by 36%, and enhanced high‐density lipoprotein by 32% in rats	Tiss et al. (2020)
		Male C57BL/6 mice	Decreased weight gain and changes in both the gut microbiota and mycobiota. The kefir administration lowers blood cholesterol and ameliorates systemic inflammation in HFD‐fed mice	Kim et al. (2014), Kim, Kim, et al. (2017)
		Male C57BL/6J mice	Reduce the buildup of intracellular lipids and epididymal adipose tissue by 19% and body weight gain. Upregulation of PPAR‐α in adipose tissue and reduction of cholesterol	Kim, Jeong, et al. (2017), Lim et al. (2017)
Fermented tea/herbs
Chinese dark tea	Extraction, fermentation with Aspergillus niger, and pasteurization	Male C57BL/6 mice	Inhibited mice from gaining weight or visceral fat and controlled the expression of genes linked to obesity	Sun et al. (2019)
Fermented RAM	Fermentation by L. plantarum	Male Sprague–Dawley rats	Fermented RAM's anti‐obesity effects are shown by preventing endotoxemia and related inflammation, influencing the distribution of intestinal microflora, and suppressing adipogenesis	Wang et al. (2015)
Fu brick tea	Microbial‐fermented tea	Male Sprague–Dawley rats	The fermented tea influenced intestinal barrier activities while lowering inflammation and oxidative stress. Additionally, it raises the gut microbiota's Firmicutes to Bacteroidetes ratio	Zhou et al. (2021)
Fermented tea	Natural fermentation	Male C57BL/6N mice	Significant reductions in body weight and adipocyte lipid accumulation	Lee et al. (2024)
Fermented vegetables/fruits/beverage
Ginseng vinegar	Two‐stage fermentation by different microbial strains	Male C57/BL6 mice	Triglycerides, total cholesterol, epididymal fat weight, and body weight gain were all considerably decreased by the addition of ginseng vinegar product	Oh et al. (2019)
Cabbage‐apple juice	Fermentation by L. plantarum EM	Sprague–Dawley rats	Consuming fermented cabbage‐apple juice regulates liver weight and overall body weight	Park et al. (2020)
Red wine pomace product	Vinification of Vitis vinifera L. cv	Male Wistar rats	Decreased lipid deposition, blood sugar, liver weight, and obesity‐related problems while boosting antioxidant status	Gerardi et al. (2020)
Huyou	Fermentation by L. plantarum L1 and Lactobacillus fermentum L2	C57BL/6J mice	Improved gut dysbiosis and increased microbial diversity. Moreover, there was a drastic decrease in Firmicutes/Bacteroidetes ratio	Yan et al. (2020)
Cabbage and apple	Fermentation by L. plantarum EM	Male Sprague–Dawley rats	This prevents metabolic disorders by regulating body weight, liver, and fat pad weights in rats. It also improves serum lipid levels by modulating leptin, insulin, adiponectin, and hepatic lipid metabolism genes	Park et al. (2020)
Fermented cereals/legumes
Fermented soybean paste (Doenjang)	NM	Human subjects	Decrease of visceral fat and anti‐obesity	Lee et al. (2012)
Chungkookjang	Fermentation by Bacillus licheniformis	Overweight/obese human subjects	Enhancement of body composition in adults who are overweight or obese	Byun et al. (2016)
Meju	Fermentation by Bacillus subtilis and Aspergillus oryzae	C57BL/6J mice	Reduces body weight gain, suppresses digestion of dietary lipids, and ameliorates hyperlipidemia	Bae et al. (2014)
Doenjang	NM	Male C57BL/6J mice	Reduced oxidative stress, corrected the dysregulated adipokine gene expression brought on by excess adiposity, and blocked the inflammatory signals coming from adipose tissue	Nam et al. (2015)
	A. oryzae	Male C57BL/6N mice	Decreases lipid levels and body weight	Park et al. (2012)
Sweet sorghum	Microbial treatment and enzymatic transformation	In vitro digestibility	Demonstrated antioxidant activities in the oligosaccharide molecules	Sharma et al. (2020)
Rice buckwheat	Probiotic‐fermentation mix (Bacillus sp. DU‐106 and L. plantarum)	C57BL/6 mice	Fermented rice buckwheat supplementation reduced body weight and dyslipidemia in HFD‐fed mice while improving oxidative stress and inflammation. It also promoted lipolysis and restored gut microbiota balance	Yan et al. (2022)
Fermented roots/tubers
Fermented ginseng	Probiotics mix (Bifidobacterium longum BORI and Lactobacillus paracasei CH88)	Male ICR mice	Increase in PPARα gene expression in both hepatic and adipose tissue	Kang et al. (2018)
	Fermentation by Leuconostoc mesenteroides KCCM 12010P	3T3‐L1 adipocyte	TNF‐α, IL‐1β, and IL‐6 were cytokine‐linked inflammation whose expression was inhibited by fermented HPG. Furthermore, the 3T3‐L1 cells suppressed lipid accumulation	Hwang et al. (2019)
Fermented Salicornia herbacea L. and Artemisia annua L.	Fermentation by Enterococcus faecium SK4369 and L. plantarum SK3494	Male C57BL/6 mice	The fermented extracts reduced body weight and inhibited adipocyte differentiation and lipid accumulation	On et al. (2023)
Jiaosu (Angelica keiskei)	Fermentation by Saccharomyces cerevisiae	Male C57BL/6J mice	The product successfully reduced mice's hepatic lipid residue, hyperlipemia, and HFD‐induced obesity	Fu et al. (2022)
Fermented Panax notoginseng	Fermentation by three selected LAB strains	Female C57BL/6 mice	The fermented product demonstrated stronger anti‐hyperlipidemic and anti‐inflammatory effects by inhibiting signaling pathways associated with appetite regulation and energy intake	Shin et al. (2021)
Fermented fish/meat
Mealworm fermented extract	Fermentation with Saccharomyces cerevisiae strain	Male C57BL/6J mice	Reduced weight loss and body fat mass. Suppressed triglyceride levels	Mun et al. (2024)
Fermented Fish (Surströmming)	Natural fermentation	Male and female adults	No detectable changes in the gut microbiome in response to Surstromming	Kallner et al. (2023)

Abbreviations: HFD, high fat diet; IL‐1 β, interleukin‐1 beta; IL‐6, interleukin 6; NM, not mentioned; PPARα, peroxisome proliferator‐activated receptor α; RAM, rhizoma atractylodis macrocephalae; TNF‐α, tumor necrosis factor.

Fermented foods contain beneficial microbes (potential probiotics) that synergistically interact with prebiotics, which act as food for the probiotics and promote their growth. This further enables them to perform their functions more effectively and create healthy gut microbiota that support metabolism and fat storage regulation. Therefore, an overview of the terms is provided along with a unanimous declaration that describes their therapeutic roles due to inaccurate portrayal of fermented foods, probiotics, prebiotics, postbiotics, and so on by industry stakeholders and literature (Balasubramanian et al., 2024; Marco et al., 2021). A scientific association named ISAPP has provided a generalized agreement (Table 2).

TABLE 2.

Nomenclature clarifications: A guide for understanding and using the correct terminology.

Terms	Description
Fermented foods	“Foods made through desired microbial growth and enzymatic conversion of food components”
Probiotics	“Live microorganisms that, when administered in adequate amounts, confer a health benefit on the host”
Prebiotics	“Fiber/substrate found in some foods that feed beneficial bacteria in the gut, promoting a healthy digestive system
Postbiotics	“Preparation of inanimate microorganisms and/or their components that confer a health benefit on the host”
Bioactives	“Naturally occurring substances in foods or plants that have an effect on biological processes in the body, promoting health benefits or influencing disease prevention”
Metabolites	“Small molecules produced during metabolism in living organisms, playing crucial roles in cellular processes and physiological functions, often acting as intermediates or end‐products”
Phytochemicals	“Bioactive compounds found in plants that contribute to their color, flavor, and disease resistance, offering potential health benefits when consumed”

Source: Adapted from Balasubramanian et al. (2024), Venegas Parada et al. (2019).

Many microorganisms cause varying biochemical reactions during the fermentation process. These reactions produce a variety of bioactives that are helpful to human health, including organic acids, phenolic compounds, short‐chain fatty acids (SCFAs), vitamins, amino acids, enzymes, exopolysaccharides, and bioactive peptides (Revuelta et al., 2018; Silva et al., 2020; Zhou et al., 2019). Fermented foods may help with obesity management because of these bioactive compounds, which can individually affect adipogenesis, control appetite, regulate lipid metabolism, insulin resistance, and inflammation as well as improve gut health and digestion (Figure 1). When combined, these compounds can have synergistic effects that magnify their individual actions, creating an effective mechanism for improving overall metabolic health.

FIGURE 1.

General antiobesogenic benefits of fermented foods. Fermented foods containing probiotics (1) can alter the gut microbiota, improving insulin sensitivity and lipid metabolism and reducing systemic inflammation. Probiotics, along with prebiotics (2), foster an environment that enhances the production of short‐chain fatty acids (SCFAs) (3) like acetate, propionate, and butyrate. These SCFAs inhibit adipocyte differentiation and promote fatty acid oxidation, potentially reducing fat storage. They exert their effects mainly through G‐protein‐coupled receptors such as FFAR2 and FFAR3 in the gut and other tissues. SCFAs also influence appetite‐regulating hormones, such as ghrelin and leptin, via brain–gut communication mediated by gut receptor activation and vagus nerve signaling. Bioactive peptides (4) from fermented foods, such as casein and soy peptides, improve insulin sensitivity and exert anti‐inflammatory effects. These peptides also reduce pro‐inflammatory cytokines and modulate microbiota composition. Polyphenols (5) in plant‐based fermented foods can modulate gut microbiota, reduce oxidative stress, and activate anti‐inflammatory PPAR pathways. They also reduce systemic inflammation by inhibiting pro‐inflammatory cytokines and enzymes.

In the context of obesity, several factors have emerged as key players, including probiotics, resistant starch, SCFAs, bioactive peptides, polyphenols, alcohols, gases, and organic acids. These components play significant roles and are further explored below.

2.1. Probiotics

Probiotics (live microorganisms) are the most direct and effective intervention because they can enhance metabolic and obesity‐related markers. They can also aid nutrient digestion and absorption, regulate glucose metabolism, lower body fat, and balance the gut flora, encouraging a balance between beneficial and detrimental bacteria (Maftei et al., 2024; Torres et al., 2024). Probiotic‐rich fermented foods include tempeh, kombucha, mahewu, miso, kefir, ogi, sauerkraut, kimchi, amasi, and yoghurt. These foods contain beneficial organisms, such as Lactobacillus rhamnosus, Lactobacillus gasseri, Bifidobacterium breve, Lactobacillus fermentum, and Lactobacillus plantarum (Fidanza et al., 2021; Million et al., 2012).

Probiotics may have an effect through a variety of ways. For instance, taking probiotics may boost the fermentation of the gut microbiota, reduce appetite, and promote fullness, all of which are associated with higher concentrations of plasma gut peptides (Million et al., 2012). Certain probiotics can also modify the gut microbiota's capacity to obtain energy from food by encouraging the synthesis of SCFAs, which results in decreased absorption of calories and fat storage (Hassan et al., 2024). Research also underscores the enhanced efficacy of multi‐strain probiotics over single‐strain formulations in weight management. From a meta‐analysis study by Zhang et al. (2015) on the effect of probiotics on body weight and body mass index, multi‐strain probiotics were more effective in reducing fat mass, body mass index, and body weight. However, despite promising evidence of probiotics’ metabolic benefits, two double‐blind studies involving obese adolescents found no significant effects (Gøbel et al., 2012; Larsen et al., 2013). This highlights the need for further investigation into the specific conditions and populations in which probiotics may exert their metabolic effects.

Ogawa et al. (2015) investigated how the probiotic L. gasseri SBT2055 inhibits lipid absorption. They proposed that L. gasseri SBT2055 may interact with bile acids, disrupting fat emulsions and causing them to merge. This decreases the oil–water interface, thereby lowering lipase efficiency. The strain demonstrated a dose‐dependent reduction in lipase activity (1–100 µg/mL) and showed superior lipid inhibition compared to four other strains. These findings suggest that L. gasseri SBT2055 has potential antiobesity effects through modulation of fat absorption and suppression of lipase‐mediated fat hydrolysis (Ogawa et al., 2015). Zhou et al. (2013) also found that L. pentosus S‐PT84, isolated from shibazuke (fermented Japanese food product), exhibited dose‐dependent pancreatic lipase activity. The strain displayed lipase inhibitory activity (LIA), indicating that postbiotics rather than live cells may contribute to this effect. The LIA in Lactobacillus strains is linked to cell wall components like S‐layer proteins and lipoteichoic acids, which enhance surface hydrophobicity. This facilitates adhesion to the oil–water interface of the enzyme, thereby modifying its properties (Zhou et al., 2013). The study further suggested that certain probiotic strains and their fermented products may reduce lipid absorption by binding to lipids in the intestine.

Another method for probiotic action is by inhibiting the proliferation and differentiation of adipocytes. Adipocytes are crucial for lipid storage and energy balance, as they store triglycerides for energy reserves. Therefore, inhibiting adipocyte growth could reduce hyperplasia and hypertrophy typically seen in obese subjects. The 3T3‐L1 preadipocyte cell line is widely used to study antiobesity effects related to adipocyte inhibition. These cells accumulate triglycerides upon differentiation due to the expression of adipocyte‐specific genes PPARγ and C/EBP‐α. These genes activate the transcription of mRNAs encoding enzymes like FAS, LPL, and ACC, which are involved in lipogenesis and adipogenesis. These enzymes play key roles in fat synthesis and lipid metabolism, making the 3T3‐L1 model useful for studying obesity mechanisms (Manzanarez‐Quin et al., 2021). Certain probiotic strains or even their cell components have been shown in multiple studies to be helpful in preventing adipogenesis in the 3T3‐L1 cell line (Kim & Lim, 2020; Lee et al., 2018; Manzanarez‐Quin et al., 2021, 2023).

Fermented foods rich in probiotics also enhance glucagon‐like peptide‐1 (GLP‐1) secretion. Drugs of the GLP‐1 class are used to treat Type 2 diabetes and, more recently, weight management (Jain et al., 2024). They function by elevating insulin secretion, reducing glucagon secretion and appetite as well as slowing gastric emptying. According to a 2015 study, the probiotic Lactobacillus reuteri SD5865 raised GLP‐1 levels in healthy men and women by 43%, an effect that took only 4 weeks to manifest (Simon et al., 2015).

2.2. Starch and resistant starch

Starch and resistant starch are both forms of carbohydrates. A composite functional component consisting of partially digested starch polymers, reducing sugars, and other biomolecules is produced by starch fermentation, whereas resistant starch (that acts more like dietary fiber than a typical starch) ferments in the large intestine after resisting digestion in the small intestine. Studies have linked higher intakes of resistant and slowly digesting starches to decreased appetite, enhanced satiety, and/or decreased body weight (Al‐Mana & Robertson, 2018; Guo et al., 2023). Research also suggests that resistant starch, a fermentable carbohydrate, shares characteristics with prebiotics (dietary fiber), potentially influencing satiety and regulating weight (Guo et al., 2023; Tekin & Dincer, 2023).

Meals high in resistant starch generated higher satiety for 2–6 h and resulted in reduced glucose and insulin responses, according to in vivo human investigations (Willis et al., 2009). Additionally, Garcia‐Vazquez et al. (2019) conducted a randomized, crossover study on 14 obese/overweight adults to assess the effects of different resistant starch treatments on appetite. They found that hunger was significantly lower and satiety higher with high amylose maize starch compared to other treatments (p < 0.05). The study concluded that high amylose maize starch may influence appetite and should be further explored for weight loss.

Other research on this subject showed either conflicting data or findings, including studies of Sanders et al. (2021) and Vetrani et al. (2018), which suggested that resistant starch did not help overweight/obese adult volunteers feel more satisfied or less hungry. For the study of Vetrani et al. (2018), they examined the impact of resistant starch (amylose‐rich wheat flour) on appetite and food intake in overweight/obese adults. Ten non‐diabetic participants (aged 34–58, BMI > 30 kg/m²) were given meals containing amylose‐rich wheat flour or conventional wheat flour. Appetite was assessed at intervals between 30 and 240 min after eating. Results showed a significant reduction in appetite after 4 h (p < 0.05) but no notable differences in hunger, satiety, or food intake between the groups (p > 0.05). The study concluded that resistant starch might reduce appetite but has limited potential for weight management in obese individuals (Vetrani et al., 2018). The García‐Vázquez study, with three sessions, found that resistant starch helped decrease appetite over time, whereas shorter studies by group of Sanders and Vetrani showed minimal or no effect on hunger or appetite (Sanders et al., 2021; Vetrani et al., 2018). These findings suggest that longer study time has a greater impact on appetite regulation.

2.3. Short‐chain fatty acids

SCFAs are key byproducts of fermentation, primarily produced by anaerobic microorganisms through the breakdown of oligosaccharides, polysaccharides, carbohydrate derivatives, proteins, peptides, and glycoprotein precursors (Anachad et al., 2023). Butyrate, acetate, and propionate are among the major SCFAs that are generated when gut bacteria ferment both amino acids and carbohydrates (Harris et al., 2021). After passing through the colon, these SCFAs may assimilate in the bloodstream and have an immediate impact on peripheral tissue function or metabolism (Anachad et al., 2023; Silva et al., 2020). Propionate, for example, influences gut hormone release, which can decrease appetite and calorie intake; butyrate, on the other hand, enhances insulin sensitivity and decreases fat formation by modifying energy consumption and fat storage pathways (Lin et al., 2012).

The brain's regulation of metabolic disorders by SCFAs (particularly propionate and butyrate) involves the activation of FFAR2 and FFAR3 receptors (Silva et al., 2020). Activation of these receptors reduces the activity of orexigenic neurons in the hypothalamus that express neuropeptide Y (Li et al., 2018), as well as modulates signaling via the ghrelin receptor (Torres‐Fuentes et al., 2019), both of which play a role in regulating circadian rhythms and appetite. Rodent studies suggest that administering prebiotics that shift the gut microbiome toward higher butyrate production can have beneficial effects, including increased GLP‐1 levels (Barrea et al., 2019; Silva et al., 2020) and greater hypothalamic expression of pro‐opiomelanocortin (Ahmadi et al., 2019), thereby influencing the hunger‐satiety cycle. Although these findings are somewhat limited, human studies also support these effects, showing that acute rectal infusions of sodium acetate and SCFA mixtures raised circulating PYY levels in overweight individuals (Canfora et al., 2017; van der Beek et al., 2016).

In addition to SCFAs, fermentation produces other metabolites, such as organic acids, alcohols, and gases. These metabolites play a significant role in modulating various metabolic pathways, including those involved in fat metabolism. They also influence gut motility, microbial composition, and overall gut health, all of which are interconnected with metabolic processes (Fasogbon et al., 2023; Liu et al., 2022; Shi et al., 2022). For instance, the production of certain organic acids can alter the pH of the gut, which may affect the growth and diversity of the microbiota, potentially leading to shifts in the balance between beneficial and harmful bacteria (Berkemeyer, 2009; Dibner & Buttin, 2002). This, in turn, can impact the absorption of nutrients and the regulation of fat storage and utilization. Organic acids are a broad category of chemicals that include any organic carboxylic acids, such as fatty acids and amino acids, with the general structure R‐COOH (Khan & Iqbal, 2015). The acids most commonly linked to antiobesogenic effects are short‐chain acids (C1–C7) and certain carboxylic acids that contain a hydroxyl group (Shi et al., 2022). Moreover, fermentation‐derived alcohols and gases can influence the release of gut hormones and neurotransmitters, which are crucial for metabolic homeostasis, epithelial secretion, gut motility, and regulation of gut inflammation (Kalantar‐Zadeh et al., 2019; Liu et al., 2022).

2.4. Bioactive peptides

Bioactive peptides that range in length from 2 to 20 amino acid residues are created when dietary proteins ferment, undergo proteolytic cleavage, or mature (Chelliah et al., 2021). Numerous studies have revealed that the hydrolyzed peptide fragments from fermented food proteins contain a variety of physiologically active peptides that target various pathways, potentially enhancing physiological function and reducing the risk of obesity (Chelliah et al., 2021; Suryaningtyas & Je, 2023). Gil‐Rodríguez and Beresford (2019) assessed the LIA of fermented milks with 31 LAB strains. From the study, LIA was maintained even after removing casein and microbial cells during the filtration and fractionation process. Additionally, the activity persisted in the <3 kDa fraction, suggesting that small peptides released during fermentation inhibited lipase activity. The inhibitory mechanism likely involved enzyme inhibition at the active site by 2 kDa peptides. These findings indicate that certain LAB strains in fermented dairy products could offer potential for weight loss through pancreatic lipase inhibition. Another study by Kim and Lim (2020) isolated lipase inhibitory peptides from fermented milk toward developing a functional anti‐lipase yoghurt. These peptides were purified by ultrafiltration and chromatographic‐based separation techniques. The <1000‐dalton fraction exhibited 46.83% anti‐lipase activity and 96.10% yield after ultrafiltration (Kim & Lim, 2020).

Bioactive peptides can enhance the absorption of fat, protein, and carbohydrates by boosting the activity of digestive enzymes (Sun et al., 2020). They can influence the uptake and absorption of nutrients via interaction with intestinal receptors and transporters. Additionally, in the intestine, bioactive peptides have physiological effects. They could modify the gut microbiota's composition, encourage the growth of favorable microbes, and inhibit the harmful ones (Cao et al., 2019; Shang et al., 2021).

2.5. Polyphenols

Polyphenols are being labeled as “antiobesity” agents capable of obesity management and weight reduction (Boccellino & D'Angelo, 2020). Polyphenols, such as curcumin, catechins, flavonoids (anthocyanins, isoflavones, flavones, and flavanones), and nonflavonoids (lignans, derivatives of stilbenes, and phenolic acids), have been proposed to have positive impacts on energy and lipid metabolism (Farhat, 2024; Min et al., 2013). They also have a proven ability to reduce free radicals by their involvement in enzyme regulation and modulation processes during cellar reduction reactions and potentially on weight status. This is connected to the number and position of –OH groups present on the aromatic ring (Pisoschi et al., 2021). Polyphenols are present in various fermented foods, particularly those derived from plant sources. For example, polyphenols found in fermented tea, such as kombucha, have been associated with improved metabolic profiles (Tran et al., 2022).

It is becoming increasingly evident that probiotic fermentation can transform complex polyphenols. This will increase the concentration and make the polyphenols more bioavailable in fermented foods (Adebo & Gabriela Medina‐Meza, 2020; Escrivá et al., 2021; Hwang et al., 2021). For instance, in a mouse model of HFD‐induced obesity, a mix of polyphenol‐rich wine and kefir‐derived probiotic LAB works better than either one alone to fight obesity (Cho et al., 2018). This synergy likely occurs because probiotics facilitate the breakdown of polyphenols into simpler and free forms that can then be better absorbed by the mouse, thereby amplifying the overall health benefits of the combined treatment. Similarly, a different study discovered that a special mixture of probiotic bacterial strains and polyphenols worked better than separate treatments at lowering different signs of diet‐induced obesity (Westfall et al., 2018).

3. MECHANISMS OF ANTIOBESOGENIC EFFECTS FROM FERMENTED FOODS

The following sections provide an overview of the major mechanisms of action associated with fermented food products, focusing on their effects on biological processes such as appetite control, body weight, fat mass, and gut microbiota modulation. Although the molecular processes underlying these effects are highly diverse and not easily categorized, the studies included in this review were selected based on their relevance to these key outcomes. A summary of several fermented foods and their antiobesogenic mechanisms of action is provided in Table 3.

TABLE 3.

Fermented foods and their mechanism of action as antiobesogenic effects.

Substrate/Product	Starter	Bioactive compounds	Dosage	Model	Mechanism	References
Ginseng	Lactobacillus paracasei and Bifidobacterium longum	ND	0.25%, 0.5%, and 1%	ICR mice	The TNF‐α expression in adipose tissue ↓, body mass, adipose tissue, and hepatic lipid accumulation ↓	Kang et al. (2018)
Okara	Aspergillus sojae and Aspergillus oryzae	TPC and amino acids	20%	Mice	The expression of FASN and SREBP‐1 mRNA ↓. Increased body weight, rise in adipose tissue, and hepatic lipid accumulation ↓	Ichikawa et al. (2022)
Barley	Lactobacillus plantarum dy‐1	β‐glucan and phenolic acids	800 mg/kg or 1 g/kg	SD rats	Enhance the production of brown‐specific mRNA and UCP‐1 to potentiate brown adipose tissue thermogenesis and white adipose tissue browning	Gu et al. (2021), Xiao et al. (2019)
Douchi	Rhizopus chinensis 12	Thrombolytic enzyme	0.83 and 1.66 g/kg	SD rats	Level of high‐density lipoprotein cholesterol ↑, CYP7A1 and PPARα expression ↑, SREBP‐1 expression ↓, the serum levels of triglycerides, inflammatory factors, fat cell hypertrophy, liver damage, total cholesterol, and low‐density lipoprotein cholesterol ↑, and finally reverse the gut microbiota	An et al. (2022)
Gochujang	A. oryzae	Lysophosphatidylcholines, soyasaponin, daidzein, genistein, and capsaicin	10%	SD rats	Body weight growth, lipid accumulation, and the levels of triglycerides and total cholesterol ↓, as well as the activity of hepatic FAS, ACC, malic enzyme in the liver, and LPL in adipose tissue ↓	Son et al. (2020)
Tremella‐blueberry	Probiotics (nine kinds)	ND	25, 50, and 75 mL/kg	SD rats	Modulates the quantity of SCFA types and gut microbiota diversity ↑, triglyceride levels, body weight, and fat weight ↓	Sheng et al. (2021)
Persimmon	Red wine yeast and Monascus purpureus	Gallic acid	8% (w/w)	C57BL/6N mice	Body weight, abdominal and liver fat, serum triglycerides, total cholesterol, and glucose ↓	Song et al. (2020)
Natto	Bacillus subtilis natto	ND	2.5% and 5%	C57BL/6J mice	The Bacteroidetes/Firmicutes ratio ↑, body weight gain, adipocyte hypertrophy, and the levels of total cholesterol and triglycerides ↓	Kushida et al. (2018)
Red ginseng	L. plantarum A KFCC11611P	Ginsenoside	250 mg/kg	SD rats	The IRS‐1 and GLUT4 in the muscle ↑, the aortic protein expression of ET‐1 and adhesion molecules ↓, body and liver weight ↓	Kho et al. (2016)
Huyou juice	Lactobacillus fermentum L2 and L. plantarum L1	ND	10 mL/kg	C57BL/6 mice	Modify the intestinal flora's makeup, reduce the ratio of Firmicutes to Bacteroidetes to improve the dysbiosis of the gut, and increase the variety of KEGG and GO pathways	Yan et al. (2020)
Soybean meal	B. subtilis and L. plantarum	Isoflavones	500 and 1000 mg/kg	SD rats	The ALT, AST, creatinine, and uric acid plasma levels ↓, the plasma and hepatic levels of MDA, insulin resistance, and organ damage by oxidation ↓	Huang, Chen et al. (2022)
Kimchi	Leuconostoc mesenteroides DRC 0211	ND	10%	C57BL/6J mice	The expression of fatty acid oxidation‐related CPT1 ↑ and the mRNA expression of adipogenesis‐related genes ↓	Cui et al. (2015)
Kefir	Probiotic (Lactic acid bacteria)	Surface layer proteins	120 mg/kg	C57BL/6J mice	Increased body weight and fat mass, high cholesterol, and insulin resistance ↓. Then, 38 genes showed upregulation and 41 downregulation	Kim, Lee et al. (2021)
Tartary buckwheat	Aspergillus niger	Fiber	100 and 400 mg/kg	C57BL/6J mice	Enhance the levels of plasma lipids, liver steatosis and inflammation, FAS Caspase‐1, NLRP3 inflammasome, and SREBP1 protein expression ↓ and replenish the diversity of gut microbiota	Huang, Zhang et al. (2022)
Ougan	Lactobacillus casei Lpc37	Flavonoids	20 mL/kg	C57BL/6J mice	Weight gain on the body, fat storage, and developing liver steatosis ↓, browning of white adipose tissue ↑, and intestinal probiotics ↑	Guo et al. (2021)
Kochujang	A. oryzae	Daidzein, genistein, and capsaicin	34.5 g/day	Human	The total cholesterol level ↓	Lim et al. (2015)
Tempeh gembus	Rhizopus oligosporus	Fiber	103 and 206 g/day	Women	The levels of high‐density lipoprotein cholesterol ↑, low‐density lipoprotein cholesterol, and total cholesterol ↓	Afifah et al. (2020)
Apple	Probiotics (L. plantarum, Lactobacillus acidophilus, and L. fermentum)	Polyphenols	15 mL/kg	C57BL/6J mice	Blood lipid levels, enhanced gut microbiota, SCFA levels in cecal contents ↑, Gain in body weight, fat accumulation ↓	Han, Zhang et al. (2021)
Rhizoma	L. plantarum	ND	250 mg/kg	SD rats	Gut permeability and microbiota balance, IEBF intestinal epithelial barrier function and serum high‐density lipoprotein cholesterol ↑, triacylglycerol in serum, body weight and adipose tissue weight ↓	Wang et al. (2015)
Tempeh	R. oligosporus	ND	ND	Women	Arginine levels ↑, insulin response acyl‐ghrelin ↑	Wang, Zhang et al. (2024)
Fermented soybean extract	B. subtilis (BTD‐1)	ND	0, 50, and 100 µg/mL	3T3‐L1 preadipocytes	Glucose uptake into the adipocytes ↑ ACC protein and GLUT4 ↑, and lipid accumulations and C/EBPα expression ↓	Hwang et al. (2015)
Rice buckwheat	Probiotic (Bacillus sp. DU‐106)	Rutin, total polyphenols, total flavonoids, and γ‐aminobutyric acid	300 mg/kg	C57BL/6 mice	Body weight, hepatic lipogenesis, and cholesterol synthesis ↓, lipolysis ↑, reduce oxidative stress, chronic inflammation, and counteract gut dysbiosis ↑	Yan et al. (2022)
Fermented fruits	Probiotics (L. paracasei, Pediococcus pentosaceus, and Meyerozyma caribbica)	ND	9.0 mL/kg	C57BL/6J mice	Serum triglyceride and total cholesterol ↓, leptin level ↑, and gut microbiota dysbiosis ↑. Then, PPARγ and CPT1 expressions were upregulated, while PPARγ and aP2 expressions were downregulated	Tian et al. (2024)
Kefir	Probiotics (L. mesenteroides 4 and Lactobacillus kefiri DH5)	Prebiotic polyphenol‐rich GSF	5% and 10%	C57BL/6J mice	Systemic inflammation, adipogenesis, and insulin resistance ↓, weight gain, liver, and adipose tissue weights ↓	Cho et al. (2018)
ND	Probiotics (B. longum spp. infantis, L. fermentum, and L. plantarum)	Prebiotic polyphenol‐rich triphala	0.5% (w/v)	Wild‐type Drosophila melanogaster	Individual probiotic treatment impacts metabolic health. Both probiotic and synbiotic formulations reduce metabolic stress, insulin resistance, and lipogenesis	Westfall et al. (2018)
Fermented teas	Natural fermentation	Prebiotic polyphenol content	7.5 mL/kg	C57BL/6N mice	Body weight, cholesterol, insulin, and leptin ↓	Lee et al. (2024)

Abbreviations: ACC, acetyl‐CoA carboxylase; ALT, alanine aminotransferase; AP2, apetala 2; AST, aspartate aminotransferase; C/EBPα, CCAAT/enhancer‐binding proteins α; CPT1, carnitine palmitoyltransferase‐1; ET‐1, endothelin‐1; FASN, fatty acid synthase; GLUT4, glucose transporter 4; GO, gene ontology; GSF, grape seed flour; ICR, institute of cancer research; IEBF, intestinal epithelial barrier function; IRS‐1, insulin receptor substrate‐1; KEGG, Kyoto encyclopedia of genes and genomes; LPL, lipoprotein lipase; MDA, malondialdehyde; mRNA, messenger RNA; ND, not determined; NLRP3, nucleotide‐binding oligomerization domain‐like receptor protein 3; PPARα, peroxisome proliferator‐activated receptor α; SD, Sprague–Dawley; SREBP‐1, sterol regulatory element‐binding protein‐1; TNF‐α, tumor necrosis factor; TPC, total phenolic compound.

3.1. Gut microbiota modulation

The microbiota in the human gut is an intricate ecosystem. It is postulated that for every gram of colonic material, there are about 1014 bacterial cells from 400 to 500 distinct species (Sekirov et al., 2010). The human gut microbiota is mostly composed of two major phyla: Firmicutes and Bacteroidetes, whereas Verrucomicrobia, Actinobacteria, and Proteobacteria are among other less common phyla (Hassan et al., 2024). Consuming fermented foods can promote the growth of beneficial microbial populations in the gut, such as Prevotella, Bacteroides, Lactobacillus, and Leuconostoc spp. (Jalili et al., 2023). These microbial populations play a significant role in modulating metabolic pathways, including those involved in lipid synthesis. For example, kimchi has been shown to increase the expression of ACSL1 by altering the gut microbiota that reduces the synthesis of triglycerides and increases the breakdown of fatty acids (Nampoothiri et al., 2017).

The intake of fermented foods alters the gut microbial population and results in a decrease or increase in the variety of microbial communities, which may have a considerable impact on energy metabolism (Stiemsma et al., 2020). Following fermented food intervention, there were changes in adiposity, lipid profiles, and inflammatory markers. The likely mechanism behind this is the alteration of gut microbiota (Park et al., 2020). Although research on the effects of fermented foods on gut microbiome modulation remains inconsistent, most studies suggest that they positively impact the diversity and abundance of various microbial species in the intestine. Fermented green tea has been shown in a study to reduce the Firmicutes/Bacteroidetes and Bacteroides/Prevotella ratios in obese individuals. Notably, the shift in the Bacteroides/Prevotella ratio aligns more closely with values seen in lean subjects, which is associated with weight reduction (Seo et al., 2015). According to a different study, herbal tea fermentation increases gut commensals and lowers oxidative stress indicators and intestinal permeability consistent with an enhanced microbiota profile (Zhou et al., 2021).

Bioactive substances can alter biological processes, which can be linked to the unique abundance of different microbiome taxa (Huang, Zhang et al., 2022; Tochitani et al., 2022). Some unidentified molecular mediators that result in antiobesity effects may be the cause of the positive roles of fermented foods in the intestinal flora. Han, Zhang et al. (2021) reported that SCFA levels were increased along with the abundance of gut bacterial families like Akkermansia and Bacteroides, even though the ratio of Firmicutes to Bacteroidetes and Bacteroides to Prevotella did not significantly change.

Fermented foods like kimchi may have a mechanism of effect linked to the greater abundance of Lachnospiraceae and Ruminococcaceae impacted by the consumption of L. plantarum HAC01 and L. rhamnosus GG‐associated fermentation (Park et al., 2017). A further example of modified bacterial phyla in the intestine is the Streptococcus genus. The intake of Greek yoghurt or dairy products fermented by Lactobacillus helveticus improves the amount of Streptococcus (Perazza et al., 2020). Also giving kefir to C57BL/6 mice for a duration of 12 weeks significantly increased LAB, as well as Candida and Saccharomyces species (Kim, Kim et al., 2017).

Fermented foods, rich in bioactive metabolites and microbes, play a key role in modulating intestinal stem cell (ISC) behavior. This is a process of influencing the function and fate of ISCs to maintain or enhance the health and regeneration of the intestinal epithelium. The ISC's behavior is tightly regulated by intrinsic signaling pathways, such as the Wnt, Notch, β‐catenin, and BMP (Ma et al., 2022), and by external influences, including the dietary components (fermented foods). SCFAs, including butyrate, acetate, and propionate, have been shown to promote the proliferation of ISCs and support their differentiation into various cell types in the gut, thus enhancing intestinal regeneration and homeostasis (Ma et al., 2022; Yao et al., 2023). In addition to SCFAs, other bioactive compounds, such as peptides, polyphenols, and vitamins, may directly interact with ISC pathways. Peptides generated during fermentation could activate growth factors or signaling pathways that regulate stem cell behavior (Kuhl & Kuhl, 2013). Polyphenols found in fermented foods like kefir and sauerkraut have antioxidant and anti‐inflammatory properties that may also support ISC function by reducing oxidative stress and modulating signaling pathways involved in stem cell maintenance (Jawhara, 2024).

The gut–brain axis, the bidirectional communication between the gut and the central nervous system, can also play a role in ISC behavior (Balasubramanian et al., 2024). Thus, metabolites, including phytochemicals, can act as neurotransmitters and neuromodulators (Yu et al., 2020) that activate the immunological, neuroendocrine, nervous, and circulatory systems (which are the linking routes of the microbiota–gut–brain axis). Upon digestion, they can result in the synthesis of microbial metabolites that modulate the intestinal barrier's permeability (Scott et al., 2020) and alter the gut microbiota, which, in turn, affects gut hormone signaling. Certain hormones, such as ghrelin and leptin, regulate appetite and energy balance and impact ISC function (Adamska‐Patruno et al., 2018). Thus, by modulating these hormonal signals, fermented foods could influence both metabolic processes and ISC behavior, offering a potential pathway to mitigate obesity.

Chronic low‐grade inflammation in the gut can contribute to diseases such as obesity, colorectal cancer, and inflammatory bowel disease. Fermented foods hold significant potential in modulating gut stem cell behavior through their ability to impact inflammation and gut regeneration. They can also modulate immune responses and promote a balanced gut immune system through mucous secretion, inhibition of pathogen colonization, and production of antimicrobial peptides. This will create an optimal microenvironment for ISC function, fostering better regeneration and tissue repair in the intestinal lining (Ma et al., 2022; Shahbazi et al., 2021). This ultimately improves nutrient absorption and processing, which can lead to better energy utilization, fat storage, and metabolism, all of which contribute to antiobesogenic effects.

3.2. Regulation of adipogenesis and lipid metabolism

Increased adipose tissue mass caused by an abnormally high number of enlarged fat cells (adipocytes) is what characterizes obesity development (Horwitz & Birk, 2023). The buildup of lipids (lipogenesis) is primarily responsible for the enlarged size of adipocytes. Increased adipocyte cell number causes adipocyte precursor cells to proliferate and differentiate into mature adipocytes, a process known as adipogenesis (Horwitz & Birk, 2023). Thus, it is practicable to control the amount of adipose tissue by blocking adipogenesis, lowering lipid accumulation, enhancing lipolysis, and/or directing adipose cell apoptosis (Wen et al., 2022).

In a study by Lee et al., transcription factors and associated genes involved in adipogenesis inhibition were expressed using qRT‐PCR. This clarifies the mechanism of LAB and LAB fermented vegetable juice's antiobesity impact in mice that were given an HFD. In comparison to the pre‐adipocytic group, there was a significant increase in the mRNA expression of regulatory proteins that included PPAR‐γ, SREBP‐1c, C/EBP‐α, FABP4, and FAS (Lee et al., 2023). These proteins work in tandem to facilitate the synthesis, metabolism, and storage of lipids. They are also essential for preserving metabolic homeostasis and energy equilibrium. However, dietary administration of LAB‐fermented vegetable juice drastically decreased the expression of regulators involved in adipogenesis via modifications to adipogenic genes, regulators, and lipolytic enzymes (Lee et al., 2023). In a different study, Aspergillus oryzae GB107‐fermented soybean extract reduced lipid storage and fat accumulation in cultured 3T3‐L1 adipocytes. The treatment with fermented food extract (50 µg/mL) showed lower glycerol‐3‐phosphate dehydrogenase activity and decreased expression levels of abiogenesis‐associated genes such as adiponectin, leptin, and adipogenin (So et al., 2015).

Fermented foods have been shown to influence the expression of genes involved in lipid metabolism, leading to reduced fat storage and increased fat oxidation. DuMez‐Kornegay et al. (2024) sequenced the mRNA of animals consuming probiotic‐rich kombucha tea to better understand the host's metabolic response. According to their findings, the probiotic‐rich product altered the expression of numerous genes related to lipid metabolism, with a notable enrichment of genes associated with intestinal function. These comprise gene products involved in several facets of lipid biology, such as acdh‐1, acdh‐2, dgat‐2, fat‐5, fat‐7, lipl‐1, lipl‐2, and lipl‐3.

3.3. Reduction of appetite hormones

Controlling one's appetite is essential to managing obesity, as long‐term body weight homeostasis depends on both total energy expenditure and food intake controlled by a variety of physiological processes (Cifuentes & Acosta, 2022). Different food matrixes generate a composite array of metabolites that can affect gut, liver, and adipose tissue metabolism and, as a result, have a significant effect on neurotransmitters in the appetite‐associated hypothalamus (Ahmed et al., 2022). Fermented products can affect postprandial responses such as blood glucose, insulin, and lipids because they slow down the emptying of the stomach. For example, fermented milk A38 had a reduced stomach‐emptying effect because of its increased viscosity (Sanggaard et al., 2004). In all lipoprotein fractions, consuming A38 increased serum triglyceride concentrations more quickly while simultaneously causing them to decrease rapidly. The same trajectory was observed for the other gut hormones and intestinal peptides, including gastric inhibitory polypeptide, GLP‐1, cholecystokinin, and peptide YY (Sanggaard et al., 2004). Similar to this, it was discovered that the fullness effects of fermented soy meal strongly impacted ghrelin, insulin, and arginine, with appetite hormones and subjective appetite scores taken into account (Noer et al., 2021). This was considered in a randomized crossover study of 13 females (aged 18–20 years; BMI 25–30) with obesity. Despite hormonal differences, no change in subjective appetite or satiety was observed between the two meals (fermented/non‐fermented), suggesting reduced brain sensitivity to appetite signals in this group (Noer et al., 2021).

SCFAs as major metabolites of fermented foods have been shown to also reduce appetite (Chambers et al., 2018). Besides satiety neuropeptides, which inhibit energy intake and the activation of ACC, SCFAs also regulate appetite through central mechanisms. These include modulation of the hypothalamus and gamma‐aminobutyric acid neurons, which play a key role in appetite control (Najm Al‐Halboosi et al., 2023). Johansson et al. (2015) found substantial differences in postprandial reactions, hunger, and feelings of fullness when they assessed the appetite‐reducing effect of unfermented whole‐grain bread and refined products compared to fermented whole‐grain bread. According to the study, dietary fiber quantity was a major factor influencing fullness, suggesting that metabolites generated by fermentation were insufficient to have an effect. Another output from the same research group found that fermented sourdough rye bread significantly increased appetite. In contrast to yeast‐fermented rye bread, sourdough fermentation could break down the viscous fibers and improve bolus dispersion (Zhao et al., 2021). However, according to another study, sourdough has no influence on hunger or subsequent nutritional intake (Iversen et al., 2018). Even though sourdough impacted protein aggregation, the acidity of the sourdough bread samples was insufficient, and there was no significant difference in rye quantity in the various test pieces. The small amounts of rye consumed by all groups may explain the lack of impact on appetite (Iversen et al., 2018). Given these conflicting results, conclusions regarding the effects of fermented foods on appetite hormones and weight regulation are premature and require further investigation.

3.4. Reduction of insulin resistance and obesity‐related inflammation

A finding of increased tumor necrosis factor alpha (TNF‐α) in adipose tissue provided the first indication of obesity‐linked inflammation (Hotamisligil et al., 1995). Since then, a number of investigations have reliably demonstrated that obese humans and animals have elevated inflammation in their adipose tissue (Wu & Ballantyne, 2020). Through its upregulation in adipose tissues, TNF‐α is crucial in triggering the insulin resistance associated with obesity. Triglycerides accumulate in various tissues due to dietary fatty acid intake, leading to cell dysfunction, lipotoxicity, and alteration in metabolic pathways (Nakamura, 2024). This process is closely linked to insulin resistance, which is a common feature of metabolic syndrome (Zhao, An et al., 2023).

Insulin resistance reduces the expression of adiponectin receptors in adipose tissues, which hinders adiponectin's autocrine function and causes obese patients’ adiponectin synthesis to be inhibited (Balsan et al., 2015). Protein kinase C activation exacerbates insulin resistance, and DAG is a major modulator of lipid‐induced insulin resistance in the liver. Usually, this mechanism decreases the translocation of glucose transporters to the plasma membrane, inhibits the insulin signaling pathway, and modifies the tyrosine phosphorylation of insulin receptor substrate 1 (Balsan et al., 2015; Samuel et al., 2010). According to Perry et al. (2016), consuming fatty diet can raise the amount of acetate generated from gut flora, which, in turn, activates the parasympathetic nervous system to cause β‐cells to overproduce insulin. This can also raise the level of the hormone ghrelin, which is linked to hunger, creating a vicious loop that encourages overfeeding on fat and upsets glucose homeostasis (Balsan et al., 2015; Perry et al., 2016).

Fermented foods have since emerged as an excellent option in the fight against insulin resistance and obesity‐related inflammation through their unique combination of beneficial microbes and bioactive compounds. In a study by Hor et al. (2022), supplementing experimental mice with fermented rice significantly reduced the levels of hormones linked to glucose metabolism, hyperglycemia, and glucose tolerance. According to the study, fermented food may be crucial in reducing dyslipidemia, especially LDL‐C/VLDL‐C and triglyceride levels. This could reduce insulin resistance and improve glucose catabolism. Further, the study's mechanism of action showed that fermented food could speed up the reciprocal control of fatty acid biosynthesis (downregulating FAS) and degradation (upregulating ACO and CPT1 that are β‐oxidation rate‐limiting enzymes) (Ray et al., 2018). The expression of PPARα plays a significant role in regulating metabolic processes, particularly in response to dietary factors. PPARα regulates lipid metabolism, fatty acid oxidation, and energy homeostasis by activating genes involved in lipid transport and breakdown. In the HFD group, the increased expression of PPARα may be attributed to improved insulin sensitivity and the presence of unsaturated fatty acids, which are found in fermented foods (Samuel et al., 2010). The PPARα overexpression probably triggered the activation of UCP3, a family of mitochondrial transporters that help export fatty acids and protect mitochondria from oxidative damage caused by lipids in the HFD group (Acosta et al., 2015). In a different investigation, a yeast‐fermented garlic product was administered for 9 weeks to obese and diabetic mice fed an HFD. This prevented damage to the kidney tubules and decreased visceral fat, body mass, adipocyte diameters, periovarian fat weight, blood glucose, blood urea nitrogen, alanine aminotransferase, and creatinine levels (Jung et al., 2011). Altogether, the studies showed that the supplementation enhanced β‐cell function and reversed insulin resistance (Jung et al., 2011).

Lee et al. (2018) evaluated the probiotic mechanism of L. plantarum Ln4 commonly found in Korean fermented foods and pickles. Thus, the probiotic‐rich fermented food enhanced insulin and oral glucose tolerance via increased cellular glucose uptake in 3T3‐L1 adipocytes and decreased IGFBP‐3 and MCP‐1 protein expression as well as IRS2 and AMPK mRNA expression in the liver. In another investigation, when mature fermented blueberry juice is administered to myotubes and adipocytes, it activates PPAR‐γ and AMPK and translocates GLUT4, exhibiting properties similar to insulin and glitazone. However, it has no effect on the glucose metabolism pathways related to insulin and calcium‐dependent glucose transport (Vuong et al., 2007). The bioavailability of bioactive compounds plays a major role in the efficacy of treatment because the compounds in fermented blueberry juice have an impact on glucose‐6‐phosphatase levels and glucose uptake in skeletal muscle and hepatic cells (Nachar et al., 2017). However, despite the potential benefits, the clinical efficacy of fermented foods in maintaining long‐term glucose homeostasis and reducing insulin resistance remains largely uncertain, primarily due to the limited number of studies and the wide variety of fermented foods with differing effects.

4. IN SILICO INSIGHTS

To maximize potential antiobesity benefits, researchers are using in silico assessment methods to model, forecast, and examine the relationships and interactions between bioactive substances from fermented foods and obesity‐linked metabolic pathways (de Medeiros et al., 2024; Hardinsyah et al., 2023; Sanjukta et al., 2021; Shah et al., 2024). In silico approach helps guide future experimental research and possible therapeutic applications by expediting obesity‐related assays and the discovery of novel antiobesity fermented food targets and understanding their mechanisms of action (de Medeiros et al., 2024).

In silico models provide superiority in terms of accuracy, cost‐effectiveness, and time efficiency compared to traditional experimental approaches (de Medeiros et al., 2024). For instance, although traditional experimental methods provide valuable empirical data, in silico models can offer high accuracy by simulating complex biological systems and predicting interactions at molecular level (Pei, 2024). These models are particularly useful in the early stages of drug discovery, allowing researchers to prioritize promising candidates based on predicted interactions and efficacy, potentially reducing the need for extensive in vivo or in vitro testing (Kraljevic et al., 2004). In silico methods also eliminate the need for expensive procedures like sample preparation, lab space, reagents, personnel, and equipment (Silva do Nascimento et al., 2021), and in terms of time efficiency, they can quickly analyze vast databases of chemical compounds or predict molecular interactions. This allows researchers to streamline the discovery process, rapidly testing hypotheses and narrowing down candidates for further study. Overall, although in silico models cannot entirely replace traditional experimental methods, they complement them by offering a faster, more cost‐effective, and often more accurate way to identify potential drug targets and streamline the development process.

4.1. In silico evaluation techniques

4.1.1. Molecular docking

Molecular docking is a computational technique that predicts how bioactive compounds or small molecules interact with specific molecular targets, such as enzymes or receptors. As evidenced in research studies, in silico tools through molecular docking can predict and optimize inhibitor interactions with target enzymes by modeling the binding affinities and dynamics of potential inhibitors with high precision. To carry out docking experiments, nuclear magnetic resonance, high‐resolution x‐rays, or homology‐modeled structures are needed. Many structural databases are publicly available, including ligand databases like PubChem and Protein Data Bank (Kim, Lee et al., 2021; Laskowski et al., 2018).

Notable molecular docking programs include DOCK, AutoDock Vina, LigandFit, Discovery Studio, Surflex, MOE‐Dock, ZDOCK, RDOCK, and Glide. Some of these tools and algorithms are now widely used in obesity research (Table 4) and other fields. In a study by Sanjukta et al. (2021), both ZDOCK and RDOCK programs were used as molecular docking tools to characterize antioxidant peptides of kinema fermented with Bacillus spp. The ZDOCK program predicted the binding interactions between peptides and the human myeloperoxidase (hMPO) protein. This program treated the hMPO structure as a rigid receptor and the peptides as flexible ligands, exploring all possible binding modes. The top docked poses were refined using the RDOCK program to identify the low‐energy complex structure (Sanjukta et al., 2021). Molecular docking further supports the potential of kombucha‐fermented food as a novel functional drink with antiobesity properties (Hardinsyah et al., 2023). The AutoDock tool (version 4.2) identified key functional metabolites that showed significant potential due to their high‐affinity values for the targeted receptors, including human and porcine lipase, α‐amylase, α‐glucosidase, and the FTO proteins (Hardinsyah et al., 2023).

TABLE 4.

Features and principal findings of in silico antiobesogenic research with in vivo and/or in vitro evaluation.

	In silico study		In vitro/In vivo
Fermented food product	Methodology	Principal findings	Method and dosage	Mechanism/Effects	References
Broad bean paste	CDOCKER protocol in Discover Studio	The molecular docking affinity (kcal/mol) of antioxidant peptides LY‐4 (−80.4859), LP‐5 (−52.0058), and VL‐9 (−112.787) could easily enter the binding pocket in Keap1	In vitro: isolated agent	Protective actions by lowering MDA and ROS levels on HepG2 cells that have been oxidatively damaged	Lin et al. (2024)
Fermented dark tea	AutoDockTools 1.5.7 and visualized by PyMOL software and Discovery Studio software 2019	With critical pocket amino acid residues of the molecular targets, EGCG, ECG, and CG exhibited a greater binding energy and subsequently generated hydrogen bonds and hydrophobic interactions	In vitro: tea polyphenol solutions	The activities of pancreatic lipase, cholesterol esterase, α‐amylase, and α‐glucosidase were decreased by the three major substances, EGCG, CG, and ECG	Wang, Chen et al. (2024)
Cocoa	AutoDockTools Vina 4.5 software; AdmetSAR server to predict ADMET properties	The release of peptides indicates a possible suppression of molecular targets associated with obesity, particularly pancreatic lipase	In vivo: 150 mg/kg on Wistar rats	The pancreatic lipase inhibition corresponds to the decline in the apparent rate of fat absorption	Coronado‐Caceres et al. (2020)
Kinema	RDOCK software via Discovery Studio v20.1.019295	A peptide SEDDVFVIPAAYPF produced in fermented kinema interacted with most of the catalytic residues identified in the myeloperoxidase enzyme	In vitro: Aqueous extract	The antioxidant peptides inhibited oxidative stress by blocking the myeloperoxidase enzyme	Sanjukta et al. (2021)
Probiotics (Lactiplantibacillus plantarum)	Molecular Operating Environment (MOE) 2019	Inhibition of xanthine oxidase, β‐hydroxy β‐methylglutaryl‐CoA reductase, and cyclooxygenase‐2	In vivo: 20–400 mg/kg diet on Sprague–Dawley rats	There was a decrease in the growth of body fat and a prevention of liver damage in obese mice	Elebeedy et al. (2022)
Milk	Peptide 2 database (PepSite 2) to predict the PLIA potential	The peptides released by in silico digestion showed sequences that bind enzyme active sites and inhibit lipid accumulation	In vitro: using 3T3‐L1 preadipocyte	Inhibition of the lipid accumulation in a 3T3‐L1 cell line	Manzanarez‐Quin et al. (2023)

Abbreviations: CG, catechin gallate; ECG, epicatechin‐3‐O‐gallate; EGCG, epigallocatechin gallate; MDA, malondialdehyde; PLIA, pancreatic lipase inhibitory activity; ROS, reactive oxygen species.

4.1.2. Quantitative structure–activity relationship models

Quantitative structure–activity relationship (QSAR) models predict the relationship between the chemical structure of natural compounds and their biological activity. QSAR models have been utilized to assess the relationship between the structural composition of bioactive compounds and their antiobesity capabilities to discover novel potential therapy medicines (Teixeira et al., 2023). According to predictor variables or structural representation, QSAR is currently divided into six classes (1D‐QSAR to 6D‐QSAR) (Patel et al., 2014).

3D‐QSAR builds on the classical Hansch and Free–Wilson methods by utilizing the three‐dimensional characteristics of ligands to forecast their biological activities. Thus, Li et al. (2016) created a 3D‐QSAR model that provided a concise description of the connection between polyphenolic chemicals and pancreatic lipase inhibitory actions. CoMSIA and CoMFA were the 3D‐QSAR methods used to analyze polyphenolic compounds’ structures, aiding in rational design for more potent compounds. These techniques calculate steric, electrostatic, hydrophobic, and hydrogen‐bond interactions, with CoMFA using a probe atom to detect field values and CoMSIA introducing similarity indices for comprehensive grid point calculations (Li et al., 2016). CoMSIA and CoMFA are popular computational methods in 3D‐QSAR modeling, whereas Hologram QSAR is used in the 2D‐QSAR method (Patel et al., 2014). Another study reported a comprehensive 3D‐QSAR analysis of 41 aryloxypropanolamine compounds, using CoMFA and CoMSIA to explore their antidiabetes and antiobesity properties. Based on these models, a series of compounds was designed, predicting a promising biological activity with a pEC50 value of 8.561 (Lorca et al., 2018).

Similar to the studies mentioned above, these predictive models could be performed to facilitate the identification of promising candidates in fermented foods and ensure their accuracy in predicting biological activity (antiobesity) using metrics like q ², r ², and pEC50 values. Ultimately, such computational approaches can expedite the optimization of bioactive components in fermented foods that hold therapeutic promise for obesity prevention and management.

4.1.3. Network pharmacology

Network pharmacology is an interdisciplinary approach that analyzes bioactives, proteins, genes, and diseases to unravel their mechanisms in complex biological systems. By integrating data from biological networks, it identifies key nodes and edges that could serve as therapeutic targets for preventing obesity (Oh et al., 2022; Wu, Wang et al., 2024; Zhao, Zhang et al., 2023). Databases have become crucial tools in the scientific community, providing access to substantial biological datasets. These web‐based resources are freely available to users, offering valuable insights into network pharmacology and complex microbiome (Oh et al., 2022). Examples of these databases include DrugBank, GeneCards, Therapeutic Target, ADMETlab 2.0, DisGeNET, SwissADME, String, gutMGene, and Online Mendelian Inheritance in Man (Oh et al., 2022; Wu, Wang et al., 2024).

For a network pharmacological approach, Oh et al. (2022) developed a microbiota–substrate–metabolite–target network to explore the therapeutic potential of interactions. This was done from public databases (using a stepwise workflow) to identify key targets and metabolites for obesity treatment. Their findings demonstrate that equol (a postbiotic) derived from isoflavone (a prebiotic) through Lacticaseibacillus paracasei JS1 (a probiotic) has the most stable binding affinity to IL6 (the target) when compared to four other metabolites: trimethylamine oxide, 3‐indolepropionic acid, acetate, and butyrate (Oh et al., 2022). Recently, network pharmacology was also employed to thoroughly examine seven active components of upregulated compounds (URCs) from fermented water extract of Millettia speciosa Champ and evaluate potential obesity targets (Wu, Wang et al., 2024). The authors constructed a compound–target–disease network, and the methodology also followed a stepwise workflow from public databases. Their findings offer a solid scientific basis for URCs’ prospective use of multitarget and multichannel intervention mechanisms to treat obesity‐related disorders.

4.1.4. Pathway analysis

Pathway analysis is a method that involves the interpretation of data from experiments (such as gene expression data [RNA‐Seq or microarrays], proteomics, or metabolomics) by mapping them onto known molecular pathways and networks to identify relevant biological processes, signals, and interactions. The mapping can be done using pathway databases like KEGG, Reactome, PantherDB, BioCyc, WikiPathways, GenMAPP, Pathway Ontology, and Gene Ontology that are knowledge bases for systematic annotation and visualization of gene functions (Joshi et al., 2021). Statistical tools and algorithms, like enrichment analysis, gene set enrichment analysis, pathway scoring, and network analysis, are further used to determine that pathways are significantly affected by the experimental data before final interpretation and visualization.

The pathway analysis tools have revealed how bioactive compounds from fermented foods can modulate key metabolic pathways and influence obesity‐related outcomes (Joshi et al., 2021; Wu, Wang et al., 2024). For instance, Wu, Wang et al. (2024) employed KEGG enrichment analysis and gene ontology to present a thorough and methodical depiction of the possible mechanisms of action of URCs in the treatment of obesity. This was performed using the DAVID database, and the top 10 enriched biological processes, cellular components, molecular functions, and KEGG pathways (p‐values <0.05 and <0.01) were visualized through an online bioinformatics platform. In contrast, pathway and gene ontology enrichment analysis was performed by Joshi et al. (2021) using the ToppCluster web tool (p < 0.05). They also used Mentha, a biological database that predicts interactions between proteins. This is to ultimately identify differentially expressed genes associated with obesity development, which can serve as reliable molecular biomarkers for screening, diagnosis, prognosis, and potential new therapeutic targets for obesity.

4.1.5. Molecular dynamics simulation

Molecular dynamics simulation (MDS) allows food researchers to analyze the dynamic properties of molecules, such as proteins, nucleic acids, lipids, and small molecules, in different environments (e.g., aqueous solutions, membrane interactions, or binding). Some commonly used MDS software includes GROMACS, CHARMM, AMBER, OPEP, and LAMMPS. GROMACS is a widely used MDS software originally developed by Groningen University (Department of Biophysics and Chemistry) for studying biomolecule folding and interactions. It has undergone significant algorithm optimizations, allowing it to efficiently simulate systems with hundreds of thousands to millions of particles. GROMACS is now applied in diverse fields, including liquid crystals, polymers, and biological macromolecules (Wang et al., 2022).

Recently, MDS technique was used to study angiotensin‐converting enzyme (ACE) inhibitory tripeptides derived from milk fermented with Lactobacillus delbrueckii QS306 (Wu, Li et al., 2024). This was conducted using the GROMACS program. The topology, atomic type, and charge needed for small molecules were parameterized using Amber Tools v18's AnteChamber Python parser interface, and the simulation trajectory was analyzed using root mean square deviation and fluctuation, β factor, hydrogen bonds, free energy landscapes, and binding patterns to evaluate dynamic and static interactions. The values obtained from the MDS indicated that the combination of WRP peptide and ACE was stable (Wu, Li et al., 2024). ACE is involved in the renin–angiotensin system, which can influence fat accumulation, inflammation, and insulin resistance (Ramalingam et al., 2017). Thus, using the provided mechanism, MDS can evaluate the binding affinity and stability of compounds to targets like PPAR‐γ, FTO, or AMPK, which are key obesity‐associated markers.

4.2. Case studies of in silico evaluations on fermented foods: antiobesogenic effects

Methods from the bioinformatics field have become invaluable in investigating and designing new candidates by helping to identify pharmacokinetic features and early warning signs of potential toxicity, among other benefits (Somda et al., 2023). This can be used as a first step to identify prime candidates according to standards such as specificity and affinity before moving on to more advanced processes, such as synthesis and/or tests (Giordano et al., 2022). Identifying potential targets is essential in the search for novel treatments for obesity (Glykofrydi et al., 2015). These targets can be modulated by agonists, antagonists, and inhibitors through metabolic pathways with both central and peripheral action (de Medeiros et al., 2024).

Molecular inhibition of target enzymes represents a critical strategy in drug discovery and development, aiming to modulate specific biochemical pathways associated with diseases. Docking methods are modeled, and the combination of hydrophobic, electrostatic, and steric complementarity optimizes the final complex formation (Sethi et al., 2019). For example, molecular docking studies presented a potential role of L. plantarum combined with green tea, chia seeds, and chitosan as inhibitors for xanthine oxidase, HMG‐CoA reductase, and COX‐2 (Elebeedy et al., 2022). The molecular simulation technique included affinity and binding pattern of the main bioactive ingredients against these enzymes. Consequently, the nine major compounds selected in the study showed potent interactions with their respective target enzymes, enabling us to ascertain the functions of green tea and chia seeds in averting obesity and its related problems (Elebeedy et al., 2022).

The study by Bellaver et al. (2024) proposes and elucidates the antiobesity activity of the peptides from bovine milk fermented with Lactococcus lactis strains. The peptides that showed intestinal absorption characteristics and showed no signs of toxicity or allergy were chosen. As demonstrated, it was anticipated that the LGPV and EVPMP peptides would attach to the target substrates. These biopeptides have previously demonstrated inhibitory properties on HMG‐CoA reductase and pancreatic lipase, two important lipid metabolism‐related enzymes. Through in silico study, this inhibitory effect was further aided by interactions, including hydrogen bonds, van der Waals, and even electrostatic interactions occasionally, which enhanced the peptides’ affinity for the target enzymes (Bellaver et al., 2024).

According to Sanjukta et al. (2021), the fermentation of soybeans using various starters produced peptide varieties, some of which are unique and may affect the functional properties of the finished product, “kinema.” Among these peptides, those containing antioxidative amino acids and a high GRAVY value were selected due to their ability to interact with myeloperoxidase, an enzyme known to increase oxidative stress. One such peptide, SEDDVFVIPAAYPF with a GRAVY index of 0.45, was found to interact with four catalytic residues in myeloperoxidase. By inhibiting these critical residues, the peptide effectively blocked myeloperoxidase activity, thereby reducing oxidative stress within the cells (Sanjukta et al., 2021). In another study, the mechanism of interactions between MVPYPQR and ACE in the presence of zinc, cofactors, and chloride atoms was examined using the HADDOCK software (Soleymanzadeh et al., 2019; van Zundert et al., 2016). The results revealed that the arginine residue at the C‐terminal of MVPYPQR alters the enzyme's tetrahedral geometry by forming hydrogen bonds with ACE. Zinc binds to three ACE residues—His 387, Glu 411, and His 38—and this interaction at the enzyme's active site is crucial to the peptide's inhibitory mechanism (Soleymanzadeh et al., 2019; van Zundert et al., 2016).

In silico analysis offers a powerful tool for identifying key genes involved in adipogenic differentiation, making it an exciting avenue for understanding fat metabolism and developing potential therapeutic interventions. Previous studies have shown that genes and proteins rich in adipogenesis pathways play a significant role in the in vitro differentiation of various cell types, particularly in processes related to fat cell formation (Chen et al., 2020). This is especially important because adipogenesis is a crucial step in fat metabolism, and any disruption in this process can lead to metabolic imbalances such as obesity, insulin resistance, and related conditions. Given the critical role of adipogenesis in maintaining metabolic homeostasis, it is essential to identify genes that regulate this process and explore plans to assist clinical treatment and prevent repercussions from metabolic imbalances. For instance, Yu et al. (2022) identified differentially expressed genes and made targeted screening possible using a microarray dataset containing healthy and obese patients. This finding underscores the potential of integrating in silico approaches with clinical data to develop innovative treatments for metabolic disorders, improving patient outcomes and advancing the field of metabolic research.

Research on in silico assessment of fermented foods’ antiobesogenic effects is still scarce, despite increased significance of the health benefits of these foods. Although it is well established that fermented foods include bioactive substances that may benefit metabolism, including managing obesity, there is a significant lack of comprehensive computational studies that elucidate the specific mechanisms through which these foods exert their effects. This draws attention to a significant gap in the literature and emphasizes the need for additional in silico studies using computational science (modeling and analysis) to learn more about the potential management and prevention of obesity using these foods.

4.3. Integration with in vivo and in vitro studies

In silico methodologies often have limitations, and the outcomes must be confirmed using other experimental techniques, including cell culture, in vitro, and in vivo investigations (Pamplona et al., 2022). Integrating with both in vitro and in vivo work is essential for advancing antiobesity research, as both approaches offer complementary insights that can lead to more effective treatments. In vitro studies generate initial hypotheses by using cultured cells or tissues in a controlled environment to explore cellular and molecular mechanisms such as fat cell differentiation, lipid metabolism, or gene expression, whereas in vivo studies involve live organisms, typically rodents or humans, and provide insights into how these mechanisms translate into whole‐body effects or relevance in a complex biological system by considering factors such as absorption, metabolism, and potential interactions. The standard approach used to evaluate food and food product safety is principally based on toxicological data acquired by animal experimentation. Because animal studies provide the majority of the basis for food safety evaluation and risk assessment, these processes are constrained by excessive reliance on default assumptions to handle uncertainties arising from the dearth of information relevant to humans (Schilter et al., 1996).

Researchers can integrate cellular and molecular findings from in vitro studies with physiological outcomes observed in in vivo models. However, the ultimate is in vivo studies that can identify adverse effects, assess the long‐term efficacy of interventions, and reveal whether a promising in vitro finding is practically and safely applicable to real‐world scenarios. Some studies have recognized the value of combining both in silico and in vitro and/or in vivo experiments for antiobesogenic effects (Table 4).

Figure 2 shows sequential steps and a comprehensive understanding of the computational process of antiobesogenic development in silico analysis pipeline: (1) The initial step is gathering data on obesity, comprising details on the genes, proteins, and molecular pathways. Information can be gathered by experimental methods or public databases like ArrayExpress, Gene Expression Omnibus, and Sequence Read Archive. (2) The gathered information is evaluated to determine the genes/proteins/pathways linked to obesity, the molecular mechanisms behind the process. During the gene expression analysis step, bioinformatics resources, such as Gene Ontology and MetaCyc, can classify expressed genes under diverse settings and their involvement in antiobesogenic processes. Following identification of the primary factors, the hub genes are found by examining how the factors interact, including protein–protein, target gene–miRNA, and target gene–transcription factor interactions. (3) The most connected should be chosen for experimental validation, and further employing methods like gene silencing, RT‐PCR, or other approaches. (4) Lastly by using 3D modeling techniques, the primary components chosen from fundamental research can be utilized to create new products and small therapeutic biomolecules.

FIGURE 2.

Graphical interpretation of the computational simulation process of antiobesogenic development in silico study pipeline. TF, target gene–transcription factor.

This multifaceted approach not only enhances the precision of hypothesis generation and validation but also accelerates the development of novel therapies by bridging theoretical predictions with empirical evidence. A study initially showed that cocoa protein hydrosylates inhibit pancreatic lipase using in silico and in vitro assays. The results were validated by in vivo assays using rats fed an HFD to induce obesity. The results highlight the significance of cocoa protein and its peptides in the development of functional food meant to reduce obesity and related conditions (Coronado‐Caceres et al., 2020).

5. CHALLENGES, LIMITATIONS, AND FUTURE PERSPECTIVES

Even with improvements in clinical studies for obesity and the discovery of new therapeutic targets, research limitations persist (Muller et al., 2022; Zhao et al., 2022). Obesity research involves numerous endpoints due to the complexity of the disease, making comprehensive assessment challenging. In this context, systemic and random in silico simulations generate exponentially large conformations that require thorough analysis, which may not always be feasible. Although some algorithms can enhance data processing, they are often expensive and time‐consuming. Additionally, computational resources for DNA‐based in silico docking methods remain limited and complex, further hindering progress in this area (Bassan et al., 2021; Navien et al., 2021). To overcome these challenges, hybrid approaches will be essential. This is achieved by the integration of in silico techniques with real‐world experimental data (e.g., clinical trials and animal studies) and omics technologies (genomics, transcriptomics, and metabolomics), which will provide more comprehension.

The degree of response to any dietary intervention can be induced by a number of factors, including genetic susceptibility, interactions among the gut microbiota, inflammatory responses, and environmental exposure (Stiemsma et al., 2020). In silico models may struggle to accurately capture this complexity as simplified models may overlook critical interactions or feedback mechanisms, leading to incomplete or inaccurate predictions of how fermented foods affect obesity (Barh et al., 2014). Advancements in computational power and algorithm development may allow for more sophisticated and multi‐dimensional simulations that incorporate the dynamic interplay between these factors.

Some variables, such as fermentation conditions, microbial strains, and raw materials, can greatly affect the makeup of fermented foods and their bioactive properties (Yuan et al., 2024). The dynamic metabolic pathways linked to obesity can also change in response to lifestyle, genetics, and nutrition (Harakeh et al., 2016). In silico models (e.g., the QSAR model) may not account for this variability adequately, leading to generalized or potentially misleading conclusions (Walsh et al., 2023). The models may provide a limited view of how fermented foods interact with metabolic processes over time, potentially missing out on long‐term effects or changes in response to dietary interventions. Future research could benefit from the development of advanced machine learning with deep learning and artificial intelligence algorithms capable of processing large datasets quickly and identifying meaningful patterns within complex biological systems. The use of cloud‐based computing or distributed computing platforms could facilitate faster processing of complex simulations, making it possible to simulate large‐scale population‐level interventions. Additionally, predictive modeling could be applied to optimize fermentation conditions (e.g., microbial strains, fermentation duration, and raw materials) (Di Biase et al., 2022) to aid in the design of novel, functional fermented foods and maximize their antiobesogenic properties.

The ongoing diversity of clinical trials and animal studies plays an important role in contributing to our understanding of the molecular mechanisms underlying the effects of fermented foods. These studies help to elucidate the specific benefits and drawbacks of various types of fermented foods, while also contributing to the development of more robust experimental designs for future research (Cote et al., 2018). In silico evaluations are part of a broader research context that includes ethical and regulatory considerations, particularly when translating computational findings into recommendations or interventions. Lack of sufficient experimental validation means that predictions made by in silico models may not always reflect true biological effects, leading to potential discrepancies between simulated and actual results. In this regard, there may be regulatory hurdles in applying in silico findings to real‐world dietary guidelines without comprehensive experimental validation and safety assessments. Thus, as the field progresses, it will be important to establish clear guidelines for using computational findings to develop food‐based interventions. These guidelines should address key areas, such as data quality and transparency, experimental validations, safety protocols, and personalized considerations.

6. CONCLUSION

Foods that have undergone fermentation serve as a source of microbial strains and bioactive substances with novel uses. In fact, probiotics, metabolites, and other bioactive substances supplemented through fermented foods may prove to be a promising avenue. Although research on fermented foods has increased recently, fundamental clinical trials are still required to quantify the measurable advantages of fermented foods in preventing and mitigating obesity and its related consequences. Information gathered for this study suggests that extensive in vitro and in vivo testing is still necessary, as limiting ourselves to data extrapolation would result in the missed opportunity for new discoveries, especially for compounds that have not yet been studied and/or explored. A recurring theme was observed in all the research included in the review: The therapeutic targets analyzed computationally showed in vivo/in vitro effects matched the projected results from the in silico analysis. The concordance between in vivo/in vitro data and in silico predictions supports the validity and potential benefits of this integrated strategy for further research on obesity treatment. This can allow clinical and pharmacological research to rapidly identify or design targeted agent‐specific inhibitors, paving the way for more effective and personalized therapies in the fight against obesity.

AUTHOR CONTRIBUTIONS

Abdullahi Adekilekun Jimoh: Investigation; methodology; validation; writing—original draft; visualization. Oluwafemi Ayodeji Adebo: Conceptualization; methodology; project administration; funding acquisition; writing—review and editing; visualization; supervision.

CONFLICT OF INTEREST STATEMENT

The authors declare that there are no conflicts of interest.

Jimoh, A. A. , & Adebo, O. A. (2025). Evaluation of antiobesogenic properties of fermented foods: In silico insights. Journal of Food Science, 90, e70074. 10.1111/1750-3841.70074